Literature DB >> 26259247

Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements.

Angelo Maria Sabatini, Gabriele Ligorio, Andrea Mannini, Vincenzo Genovese, Laura Pinna.   

Abstract

This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults. The downward vertical velocity was thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100% specificity (SP) and a mean prior-to-impact time of 157 ms (range 40-300 ms). The soft falls, i.e., those with downward vertical velocity above 0.55 m/s and below 1.38 m/s were analyzed post-impact. Six fall detection methods, tuned to achieve 100% SE, were considered to include features of impact, change of posture and height, singularly or in association with one another. No single feature allowed for 100% SP. The detection accuracy marginally improved when the height change was considered in association with either the impact or the change of posture; the post-impact fall detection method that analyzed the impact and the change of posture together achieved 100% SP.

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Year:  2015        PMID: 26259247     DOI: 10.1109/TNSRE.2015.2460373

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  9 in total

1.  A machine learning based sentient multimedia framework to increase safety at work.

Authors:  Gianluca Bonifazi; Enrico Corradini; Domenico Ursino; Luca Virgili; Emiliano Anceschi; Massimo Callisto De Donato
Journal:  Multimed Tools Appl       Date:  2021-05-15       Impact factor: 2.757

2.  An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

Authors:  I Putu Edy Suardiyana Putra; James Brusey; Elena Gaura; Rein Vesilo
Journal:  Sensors (Basel)       Date:  2017-12-22       Impact factor: 3.576

3.  Improving Fall Detection Using an On-Wrist Wearable Accelerometer.

Authors:  Samad Barri Khojasteh; José R Villar; Camelia Chira; Víctor M González; Enrique de la Cal
Journal:  Sensors (Basel)       Date:  2018-04-26       Impact factor: 3.576

Review 4.  Elderly Fall Detection Systems: A Literature Survey.

Authors:  Xueyi Wang; Joshua Ellul; George Azzopardi
Journal:  Front Robot AI       Date:  2020-06-23

Review 5.  REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.

Authors:  Maryam Pishgar; Salah Fuad Issa; Margaret Sietsema; Preethi Pratap; Houshang Darabi
Journal:  Int J Environ Res Public Health       Date:  2021-06-22       Impact factor: 3.390

Review 6.  Pre-impact fall detection.

Authors:  Xinyao Hu; Xingda Qu
Journal:  Biomed Eng Online       Date:  2016-06-01       Impact factor: 2.819

7.  Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements.

Authors:  Chih-Lung Lin; Wen-Ching Chiu; Ting-Ching Chu; Yuan-Hao Ho; Fu-Hsing Chen; Chih-Cheng Hsu; Ping-Hsiao Hsieh; Chien-Hsu Chen; Chou-Ching K Lin; Pi-Shan Sung; Peng-Ting Chen
Journal:  Sensors (Basel)       Date:  2020-10-12       Impact factor: 3.576

8.  Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Authors:  Tae Hyong Kim; Ahnryul Choi; Hyun Mu Heo; Hyunggun Kim; Joung Hwan Mun
Journal:  Sensors (Basel)       Date:  2020-10-28       Impact factor: 3.576

9.  A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.

Authors:  Xiaoqun Yu; Jaehyuk Jang; Shuping Xiong
Journal:  Front Aging Neurosci       Date:  2021-07-16       Impact factor: 5.750

  9 in total

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